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--- |
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base_model: bigcode/starencoder |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: stack-edu-classifier-javascript |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# stack-edu-classifier-javascript |
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This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/bigcode/starencoder) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3612 |
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- Precision: 0.5135 |
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- Recall: 0.3322 |
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- F1 Macro: 0.3711 |
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- Accuracy: 0.6277 |
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- F1 Binary Minimum3: 0.5704 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 256 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 512 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 | |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:| |
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| No log | 0 | 0 | 5.6298 | 0.0010 | 0.1667 | 0.0020 | 0.0059 | 0 | |
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| 0.3853 | 1.4493 | 1000 | 0.3886 | 0.4945 | 0.3110 | 0.3354 | 0.6037 | 0.5761 | |
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| 0.3791 | 2.8986 | 2000 | 0.3729 | 0.5041 | 0.3090 | 0.3395 | 0.6208 | 0.5716 | |
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| 0.3722 | 4.3478 | 3000 | 0.3720 | 0.5261 | 0.3116 | 0.3440 | 0.6189 | 0.5673 | |
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| 0.3751 | 5.7971 | 4000 | 0.3704 | 0.5247 | 0.3204 | 0.3565 | 0.6199 | 0.5766 | |
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| 0.3651 | 7.2464 | 5000 | 0.3718 | 0.5113 | 0.3352 | 0.3678 | 0.6310 | 0.5161 | |
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| 0.3695 | 8.6957 | 6000 | 0.3649 | 0.5055 | 0.3253 | 0.3607 | 0.6249 | 0.5632 | |
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| 0.361 | 10.1449 | 7000 | 0.3647 | 0.5042 | 0.3236 | 0.3571 | 0.6354 | 0.5410 | |
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| 0.3666 | 11.5942 | 8000 | 0.3764 | 0.5290 | 0.3371 | 0.3752 | 0.6146 | 0.5941 | |
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| 0.3563 | 13.0435 | 9000 | 0.3617 | 0.5179 | 0.3356 | 0.3743 | 0.6303 | 0.5674 | |
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| 0.3735 | 14.4928 | 10000 | 0.3663 | 0.4998 | 0.3423 | 0.3760 | 0.6340 | 0.5320 | |
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| 0.349 | 15.9420 | 11000 | 0.3616 | 0.5063 | 0.3306 | 0.3681 | 0.6273 | 0.5696 | |
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| 0.3679 | 17.3913 | 12000 | 0.3632 | 0.5078 | 0.3396 | 0.3786 | 0.6252 | 0.5762 | |
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| 0.3622 | 18.8406 | 13000 | 0.3612 | 0.5135 | 0.3322 | 0.3711 | 0.6277 | 0.5704 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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